The overarching goal of the proposed research is to understand the specific roles of different brain areas in the learning of executive function, by empirically testing and further developing a biologically-based computational model of the prefrontal cortex and associated subcortical systems (including the basal ganglia and midbrain dopaminergic nuclei). We focus on two specific issues: (a) the learning of abstract, rule-like representations in prefrontal cortical areas, which support flexible behavior by enabling better generalization to novel circumstances;and (b) the mechanisms of feedback-driven learning, which shape the adaptive modulation of prefrontal executive function representations by the basal ganglia, according to our model. Several predictions from this computational modeling framework have already been successfully tested in diverse populations, including Parkinson's patients and people with ADHD, both of which are thought to involve disorders of the dopaminergic system as it affects the basal ganglia and prefrontal cortex. Thus, this model has important implications for understanding the neural basis of executive function, both in neurologically intact and disordered populations. We propose to test the following hypotheses: [unreadable] Specific Aim 2.1: Factors Affecting Learning of Representations in Prefrontal Cortex: First, we test in both young adults and children a set of predictions from our computational model. For example, blocked training should facilitate the development of abstract, rule-like representations, which in turn support better generalization to novel task contexts. Second, because the degree of abstraction learning depends on the duration of active maintenance in our model, different regions of PFC may be organized according to relative degree of abstraction, and corresponding maintenance duration. We explore this idea in the model. [unreadable] Specific Aim 2.2: Factors Affecting Feedback Learning. We provide a more direct test of dopaminergic mediation of event-related potential signals responsive to feedback information (ERN) by administering dopamine D2 receptor agonists/antagonists, which should have dissociable effects on these signals according to our model. We also test whether mood induction can shift the balance of feedback responsiveness, as measured by ERP's. Finally, we attempt to disentangle multiple factors influencing learning of executive function tasks by using coordinated executive function and negative feedback learning studies in children.